Downloaded from the Gene Expression Omnibus to Uncover Diferentially Expressed Long Non-Coding Rnas (Lncrnas), Mrnas, and Micrornas (Mirnas)

Shi and Yao BMC Med Genomics (2021) 14:133 https://doi.org/10.1186/s12920-021-00931-0

RESEARCH ARTICLE Open Access Signature RNAS and related regulatory roles in type 1 diabetes mellitus based on competing endogenous RNA regulatory network analysis Qinghong Shi1 and Hanxin Yao2*

Abstract Background: Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods: Expression profles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover diferentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results: In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were signifcantly diferentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM- related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion: The identifed signature RNAs may serve as important regulators in the pathogenesis of T1DM. Keywords: T1DM, LncRNAs, CeRNAs

Background of T1DM is rising worldwide, with more than 80% of dia- Recently, type 1 diabetes mellitus (T1DM) is a multi- betes occurring in younger children [2, 3]. factorial autoimmune disease characterized by insulin Many eforts have been made recently to gain insights defciency and hyperglycaemia, which is considered to into the pathogenesis of T1DM. Tree main regions on involve the selective attack of insulin-producing pancre- chromosomes, including the protein tyrosine-phos- atic β cells by activated T lymphocytes that recognize phatase non-receptor-type 22 region on chromosome their autoantigens [1]. T1DM accounts for approximately 1p13, the human leukocyte antigen region on chro- 5–10% of all cases of diabetes mellitus, and the incidence mosome 6p21, and the insulin region on chromosome 11p15, play essential roles in insulin expression, immune *Correspondence: [email protected] response, and β-cell function, which are associated with 2 Department of Clinical Laboratory, The First Hospital of Jilin University, T1DM [4, 5]. More than 50 genomic risk loci have been No. 1, Xinmin Street, Chaoyang District, Changchun 130021, Jilin, China identifed for T1DM based on genome-wide association Full list of author information is available at the end of the article

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studies [5]. However, most of the risk loci are located in profles retrieved from the Gene Expression Omnibus non-coding genomic regions, and an increasing num- [17]. Subsequently, ceRNA-based transcriptional sig- ber of studies have focused on the potential roles of natures were revealed via the construction of a T1DM- long non-coding RNAs (lncRNAs) in pancreatic islets related ceRNA regulatory network. and the pathogenesis of T1DM [6, 7]. Motterle et al. found that some lncRNAs are modulated by proinfam- Methods matory cytokines during the development of T1DM in Data source and annotation non-obese diabetic mice, which probably contributes to Expression and non-coding RNA profles (GEO accession the sensitization of β cells to apoptosis and failure dur- number: GSE55100) deposited by Yang et al. [16] were ing the initial phases of T1DM [7]. Te nuclear-enriched downloaded from the National Center for Biotechnol- β-cell lncRNA PLUTO may regulate the expression of ogy Information GEO (https://www.ncbi.nlm.nih.gov/ PDX1, which is a key pancreatic β-cell transcription fac- geo/) [18], which consist of two subseries, GSE55098 and tor; furthermore, knockdown of PLUTO is associated GSE55099. Te GSE55098 contains expression data of with the downregulation of PDX1 in EndoC-βH1 cells peripheral blood mononuclear cells from 12 patients with and primary islet cells, implicating the roles of lncRNAs T1DM and 10 normal control subjects, which were based in the regulation of β-cell-specifc transcription factors on the GPL570 [HG-U133_Plus_2] Afymetrix Human [8]. Terefore, examining the ability of lncRNAs to regu- Genome U133 Plus 2.0 Array platform. Te GSE55099 late gene expression and cell-specifc transcription fac- contains microRNA expression data from peripheral tors opens avenues to a better understanding of T1DM blood mononuclear cells from the same 12 patients with pathogenesis. T1DM and 10 normal control subjects, based on the On the other hand, an increasing body of evidence indi- GPL8786 [miRNA-1] Afymetrix Multispecies miRNA-1 cates that microRNAs (miRNAs) play important roles in Array platform. Te miRNAs, lncRNAs, and mRNAs in processes involved in the pathogenesis of T1DM, includ- the downloaded profles were annotated via the Human ing immune system functions and β-cell metabolism and Genome Organization Gene Nomenclature Committee death [9, 10]. Assmann et al. have suggested that 11 cir- (http://www.genenames.org/) [19], where over 40,000 culating miRNAs (miR-21-5p, miR-24-3p, miR-100-5p, approved gene symbols have been recorded, of which miR-146a-5p, miR-148a-3p, miR-150-5p, miR-181a-5p, more than 19,000 are for protein-coding genes. miR-210-5p, miR-342-3p, miR-375, and miR-1275) are consistently dysregulated in T1DM patients [11]. It has Identifcation of diferentially expressed RNAs been further revealed that fve miRNAs (miR-103a-3p, Te diferentially expressed RNAs (DERs) between miR-155-5p, miR-200a-3p, miR-146a-5p, and miR- T1DM and normal controls were screened with Limma 210-3p), which have been confrmed as dysregulated (Linear Models for Microarray Data) package (Version miRNAs based on plasma miRNA expression profles of 3.34.0; https://bioconductor.org/packages/release/bioc/ T1DM patients and control individuals, could regulate html/limma.html) [20] of R3.4.1. Te cut-of criteria genes involved in the innate immune system-, MAPK-, were set as false discovery rate (FDR) less than 0.05 and apoptosis-, insulin-, and cancer-related pathways [12]. |log2 fold change| greater than 0.5. Two-way hierarchi- Additionally, it is widely acknowledged that competing cal clustering analysis based on Euclidean distance was endogenous RNAs (ceRNAs) can interact with mRNAs executed for all the identifed DERs via the pheatmap by competing with miRNAs, and miRNA-mediated (Version 1.0.8, https://cran.r-project.org/web/packages/ interactions between lncRNAs and mRNAs occur in pheatmap/index.html) of R3.4.1 [21–23]. Meanwhile, the progression of various diseases [13–15]. However, gene ontology (GO) functional enrichment in terms of few current studies have reported on the ceRNA-based biological process as well as Kyoto Encyclopedia of Genes regulatory mechanisms of T1DM. In the study of Yang and Genomes (KEGG) pathway enrichment analysis were et al. [16] global miRNA and mRNA expressions were conducted for diferentially expressed mRNAs via the profled in peripheral blood mononuclear cells from 12 Database for Annotation, Visualization and Integrated patients with newly diagnosed T1DM and 10 normal Discovery (DAVID, Version 6.8, https://david.ncifcrf. controls, while miRNA-mediated interactions between gov/) [24, 25], with a threshold of p < 0.05. lncRNAs and mRNAs were not revealed. Tus, further studies aimed at clarifying ceRNA-based transcriptional Construction of ceRNA regulatory network signatures are needed to provide new insights into the Te regulatory interactions between diferentially pathogenesis of T1DM. In our present study, diferen- expressed lncRNAs and miRNAs were retrieved from tially expressed lncRNAs, mRNAs, and miRNAs between the DIANA-LncBase (Version 2, http://carolina.imis. T1DM and normal controls were identifed based on athena-innovation.gr/diana_tools/web/index.php) [17]. Shi and Yao BMC Med Genomics (2021) 14:133 Page 3 of 10

Te negative regulatory interactions with a miRNA tar- Results get gene score (miTG-score) larger than 0.6 (the default Data annotation and DER screening threshold in DIANA-LncBase) were retained to con- According to the platform annotation information, struct the lncRNA–miRNA regulatory network, which 946 lncRNAs, 597 miRNAs, and 10,085 mRNAs were was visualized with Cytoscape (Version 3.6.1, https:// received. By comparing the sample profles of patients cytoscape.org/) [26]. with T1DM with those of normal controls, 926 DERs Te target genes of diferentially expressed miRNAs were identifed using Limma package, including 847 were obtained by using starBase (Version 2.0, http:// mRNAs (448 downregulated and 399 upregulated), 41 starbase.sysu.edu.cn/), which contains prediction data lncRNAs (22 downregulated and 19 upregulated), and 38 from the fve databases of targetScan, picTar, RNA22, miRNAs (18 downregulated and 20 upregulated; Fig. 1). PITA, and miRanda [25]. Te negative regulatory inter- Moreover, the hierarchical clustering analysis revealed actions predicted in at least one of the fve databases that all mRNAs and lncRNAs identifed could adequately were retained to construct the miRNA–mRNA regu- distinguish between T1DM samples and normal samples latory network, which was visualized with Cytoscape (Fig. 1a). Meanwhile, T1DM samples could also be dis- (Version 3.6.1, https://cytoscape.org/) [26]. criminated from normal samples based on the expression Afterwards, the lncRNA–miRNA–mRNA (1) regula- levels of the 38 diferentially expressed miRNAs (Fig. 1b). tory network was constructed by integrating the above Te GO functional enrichment analysis indi- lncRNA–miRNA regulatory interactions with the cated that the above diferentially expressed mRNAs miRNA–mRNA regulatory interactions, and visualized were signifcantly associated with the biological pro- with Cytoscape (Version 3.6.1, https://cytoscape.org/) cesses of cellular defense response (p = 3.020E−07), [26]. Moreover, GO functional enrichment in terms immune response (p = 4.320E−07), regulation of of biological process and pathway enrichment analy- immune response (p = 2.730E−06), innate immune sis were conducted for genes in the ceRNA regulatory response (p = 4.610E-06), cell surface receptor sign- network via DAVID (Version 6.8, https://david.ncifc aling pathway (p = 8.640E−06), adaptive immune rf.gov/) [24, 25], and p < 0.05 was used as the cut-of response (p = 2.170E−05), and infammatory response criterion. (p = 5.040E−05; Additional fle 1: Table S1, Fig. 2a). At the same time, diferentially expressed mRNAs were enriched in 18 KEGG pathways, such as osteoclast dif- T1DM‑related ceRNA regulatory network ferentiation (p = 4.930E−04), natural-killer-cell-mediated Te miRNAs associated with T1DM were obtained cytotoxicity (p = 6.850E−04), antigen processing and by using the Human microRNA Disease Database presentation (p = 1.008E−02), cytokine–cytokine recep- (HMDD; Version 3.2, http://www.cuilab.cn/hmdd) tor interaction (p = 1.250E−02), and glycerophospholipid [27], with the option “Disease name” flled with “type metabolism (p = 1.520E−02; Additional fle 1: Table S1, 1 diabetes mellitus.” Te disease-related miRNAs were Fig. 2b). mapped onto the ceRNA regulatory network to reveal the target genes for pathway enrichment analysis via Constructed ceRNA regulatory network DAVID (Version 6.8, https://david.ncifcrf.gov/) [24, 25] Te regulatory interactions between diferentially and p < 0.05 was used as the cut-of criterion. In addi- expressed lncRNAs and diferentially expressed miRNAs tion, the sequences of interested lncRNAs and miR- were acquired by using the DIANA-LncBase version 2 NAs were downloaded from Ensembl genome browser database. Te 94 pairs of negative lncRNA–miRNA regu- (http://asia.ensembl.org/index.html) and miRBase latory interactions with a coefcient larger than 0.6 were (http://www.mirbase.org/), respectively. Binding sites retained to build the lncRNA–miRNA regulatory net- between lncRNAs and miRNAs were predicted by work, which consisted of 61 nodes, 36 miRNAs, and 25 using miRanda (http://cbio.mskcc.org/miRNA2003/ lncRNAs (Additional fle 2: Figure S1). Te target genes miranda.html) [28], which is an algorithm for fnding of diferentially expressed miRNAs were predicted using genomic targets for microRNAs. starBase. Afterwards, the 915 pairs of negative miRNA– On the other hand, the KEGG pathways associated mRNA regulatory interactions involving diferentially with T1DM were screened from the Comparative Toxi- expressed mRNAs were submitted to construct the cogenomics Database update 2019 (http://ctd.mdibl.org/) miRNA–mRNA regulatory network, which consisted [29], with the keyword “type 1 diabetes mellitus.” Te of 322 nodes, 23 miRNAs, and 299 mRNAs (Additional enriched KEGG pathways from DAVID were compared fle 3: Figure S2). with those obtained from the Comparative Toxicog- By integrating the lncRNA–miRNA regulatory inter- enomics Database (CTD). actions with miRNA–mRNA regulatory interactions, Shi and Yao BMC Med Genomics (2021) 14:133 Page 4 of 10

a b

Fig. 1 Identifcation and hierarchical clustering analysis of diferentially expressed RNAs (DERs) in peripheral blood mononuclear cell samples between 12 type 1 diabetes mellitus (T1DM) patients and 10 normal control individuals. a Identifed diferentially expressed lncRNAs and mRNAs

with thresholds of FDR less than 0.05 and |log2 fold change (FC)| greater than 0.5. Blue dots represent downregulated DERs and red dots represent upregulated DERs. b Identifed diferentially expressed miRNAs with thresholds of FDR < 0.05 and |log2FC|> 0.5. Blue dots represent downregulated DERs and red dots represent upregulated DERs

the lncRNA–miRNA–mRNA (1) regulatory network processes, such as cell maturation (p = 2.434E−03), was established (Fig. 3). Tere were 360 nodes and negative regulation of transcription, DNA templated 1,002 regulatory interactions in the ceRNA regula- (p = 2.598E−03), negative regulation of fat cell difer- tory network, including 12 lncRNAs, 10 miRNAs, and entiation (p = 4.295E−03), and small GTPase–medi- 24 mRNAs. Te mRNAs in the ceRNA regulatory net- ated signal transduction (p = 5.980E−03; Fig. 4a, work were substantially related to 14 GO biological Additional fle 4: Table S2). Meanwhile, eight KEGG Shi and Yao BMC Med Genomics (2021) 14:133 Page 5 of 10

Fig. 2 Gene ontology (GO) functional enrichment in terms of biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for diferentially expressed mRNAs. a Enriched GO terms for diferentially expressed mRNAs. b Enriched KEGG pathways for diferentially expressed mRNAs Shi and Yao BMC Med Genomics (2021) 14:133 Page 6 of 10

Fig. 3 The constructed competing endogenous RNA (1) regulatory network. Square, triangle, and circle nodes represent lncRNAs, miRNAs, and mRNAs, respectively. Nodecolors range from green to red, which indicate downregulated to upregulated expression changes

Fig. 4 GO functional enrichment in terms of biological process and KEGG pathway enrichment analysis for mRNAs in the ceRNA regulatory network. a Enriched GO terms for mRNAs in the ceRNA regulatory network. b Enriched KEGG pathways for mRNAs in the ceRNA regulatory network

pathways were enriched for the mRNAs in the ceRNA T1DM‑related ceRNA regulatory network regulatory network, including the PI3K-Akt signaling Te miRNAs related to T1DM were downloaded from pathway (p = 1.442E−02), the Ras signaling pathway HMDD Version 3.2 with the disease name “type 1 dia- (p = 1.753E−02), and the TGF-beta signaling pathway betes mellitus,” and 32 miRNAs were obtained. By (p = 2.579E−02; Fig. 4b, Additional fle 4: Table S2). comparing with the miRNAs in the ceRNA regulatory Shi and Yao BMC Med Genomics (2021) 14:133 Page 7 of 10

network, two overlapping miRNAs (hsa-miR-181a and pathways, including the phosphatidylinositol signaling hsa-miR-1275) were retained, and the regulatory net- system, dopaminergic synapse, and the insulin signal- work involving these two miRNAs was separated from ing pathway. Te disease pathway network showed that the ceRNA regulatory network (Fig. 5a). Te genes in the four downregulated lncRNAs (LINC01278, TRG-AS1, separated regulatory network were signifcantly enriched MIAT, and GAS5-AS1) could regulate overexpressed in seven pathways, including the phosphatidylinositol hsa-miR-181a, which had six downregulated target genes signaling system (p = 4.567E−04), inositol phosphate that were involved in the three KEGG pathways (Fig. 5b). metabolism (p = 2.504E−03), mucin type O-Glycan PIP4K2A, INPP4A, PIP4K2C, and CALM1 were involved biosynthesis (p = 1.029E−02), the ErbB signaling path- in the phosphatidylinositol signaling system. CBLB and way (p = 2.636E−02), glycerophospholipid metabolism CALM1 were related to the insulin signaling pathway. In (p = 2.842E−02), dopaminergic synapse (p = 3.634E−02), the dopaminergic synapse, CALM1 and PPP2R5C were and the insulin signaling pathway (p = 3.856E−02; enriched. Table 1). Binding sites of hsa-miR-181a with GAS5-AS1, LINC01278, and MIAT were exhibited in Additional Discussion fle 5: Figure S3. Te possible binding sites of hsa- Selective destruction of insulin-producing pancreatic miR-1275 with LINC01410 were shown in the Additional islet β cells causes T1DM, a chronic immune-mediated fle 6: Figure S4. and infammatory disease [30]. In recent years, increas- Te KEGG pathways related to T1DM were further ing eforts have been made to understand the molecu- searched in the CTD with the keyword “type 1 diabetes lar mechanisms of the pathogenesis of T1DM; and, mellitus,” and 148 KEGG pathways were revealed. Tree researching ceRNA regulatory network-based RNA sig- overlapping KEGG pathways were revealed by comparing natures may contribute to gaining further insights into these 148 KEGG pathways with the above seven enriched its specifc regulatory roles in T1DM. In our study, 926

Table 1. Seven signifcantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for mRNAs in type 1 diabetes mellitus (T1DM) related competing endogenous (ceRNA) regulatory network Term Count P value Genes

*hsa04070:Phosphatidylinositol signaling system 4 4.567E 04 INPP4A, PIP4K2A, PIP4K2C, CALM1 − hsa00562:Inositol phosphate metabolism 3 2.504E 03 INPP4A, PIP4K2A, PIP4K2C − hsa00512:Mucin type O-Glycan biosynthesis 2 1.029E 02 GALNT2, GALNT11 − hsa04012:ErbB signaling pathway 2 2.636E 02 CBLB, EREG − hsa00564:Glycerophospholipid metabolism 2 2.842E 02 GPD1L, LPCAT1 − *hsa04728:Dopaminergic synapse 2 3.634E 02 PPP2R5C, CALM1 − *hsa04910:Insulin signaling pathway 2 3.856E 02 CBLB, CALM1 − *The pathway overlapping with T1DM-related pathways retrieved from Comparative Toxicogenomics Database (CTD)

Fig. 5 T1DM-related ceRNA regulatory network and disease pathway network. a The T1DM-related ceRNA regulatory network. b The disease pathway network. Square, triangle, and circle nodes represent lncRNAs, miRNAs, and mRNAs, respectively. Nodecolors range from green to red, which indicate downregulated to upregulated expression changes Shi and Yao BMC Med Genomics (2021) 14:133 Page 8 of 10

DERs were identifed in expression profles of periph- Moreover, six target genes (PIP4K2A, INPP4A, eral blood mononuclear cells from T1DM patients, PIP4K2C, CALM1, CBLB, and PPP2R5C) of hsa-miR- including 847 mRNAs, 41 lncRNAs, and 38 miRNAs. In 181a were involved in a further established disease the study of Yang et al. [16] 24 miRNAs and 1218 genes pathway network based on the T1DM-related ceRNA were found diferently expressed in patients with newly regulatory network. T1DM arises from autoimmune- diagnosed T1DM. Expression levels of hsa-let-7a, hsa- mediated β-cell destruction, and this defect is closely miR-1275, hsa-miR-22, hsa-miR-25, hsa-miR-28, and associated with the molecular levels found in the insu- hsa-miR-486, which were identifed as diferentially lin signaling pathway [36, 37]. Casitas B-lineage lym- expressed miRNAs by Yang et al., were also found sig- phoma b (CBLB), a member of the CBL/SLI family of nifcantly diferent in our study. Liu et al. [31] have ubiquitin-protein ligases, functions as a key regulator of downloaded GSE55100 and diferentially expressed lymphocyte activation and autoimmunity [38, 39]. Kom- miRNA-mRNA interactions were unveiled from the 7 eda diabetes-prone rats, which are a spontaneous animal miRNAs (hsa-miR-374a, hsa-miR-146b, hsa-miR-181a, model of human type 1 diabetes, as well as CBLB-def- hsa-miR-19b, hsa-miR-125b, hsa-let-7f, and hsa-miR28) cient mice, have been observed to have infltrations of and 651 mRNAs. In our study, diferentially expressed lymphocytes into pancreatic islets, the thyroid gland, and miRNA-mRNA regulatory interactions involved 23 kidneys, suggesting that CBLB dysfunction leads to auto- miRNAs and 299 mRNAs, and 4 miRNAs (hsa-miR- immune processes [40, 41]. Transgenic complementation 374a, hsa-miR-181a, hsa-miR-125b, and hsa-miR28) with wild type CBLB greatly suppresses the development were consistent with the previous study. In order to of the Komeda diabetes-prone phenotype, indicating revel potential hub genes involved in the pathogenesis that CBLB is a negative regulator of autoimmunity and a of Chinese type 1 diabetic patients, GSE55100 has been susceptibility gene for T1DM in the rat [42]. Moreover, also downloaded to identify DEGs, and thirteen hub one single nucleotide polymorphism in exon 12 of the genes (MMP9, ARG1, CAMP, CHI3L1, CRISP3, SLPI, CBLB gene has been shown to be associated with T1DM LCN2, PGLYRP1, LTF, RETN, CEACAM1, CEACAM8, based on analysis of a large Danish T1DM database of and MS4A3) were retrieved by module analysis [32]. 480 families [43]. In our study, expression levels of CBLB Tese thirteen hub genes were also identifed as difer- were also lower in the peripheral blood mononuclear cell entially expressed genes in our study. profles of T1DM patients. Tus, CBLB is a key gene of Besides, two miRNAs (hsa-miR-181a and hsa- T1DM, and downregulated CBLB participating in the miR-1275) were screened as T1DM-related miRNAs insulin signaling pathway may contribute to the autoim- based on information from the HMDD to construct the mune disease of T1DM. In addition, regulatory interac- T1DM-related ceRNA regulatory network. Te genes in tions between four lncRNAs (LINC01278, TRG-AS1, the T1DM-related ceRNA regulatory network were sig- MIAT,and GAS5-AS1) and hsa-miR-181a were revealed nifcantly enriched in seven pathways, and three over- in the T1DM-related ceRNA regulatory network. How- lapping pathways, including the phosphatidylinositol ever, there have been no reports, to our knowledge, of signaling system, dopaminergic synapse, and the insu- the roles of LINC01278, TRG-AS1, MIAT, and GAS5-AS1 lin signaling pathway,were revealed by comparing with in the pathogenesis of T1DM. According to the ceRNA T1DM-related pathways in the CTD. Te disease path- hypothesis, it can be surmised that these four lncRNAs way network contained one upregulated miRNA (hsa- (LINC01278, TRG-AS1, MIAT, and GAS5-AS1) might miR-181a) and four lncRNAs (LINC01278, TRG-AS1, participate in regulating the expression levels of target MIAT, and GAS5-AS1). Overexpression of miR-181a lev- genes of hsa-miR-181a by competing with hsa-miR-181a. els were found in insulin-resistant cultured hepatocytes, and inhibition of miR-181a may lead to increased protein Conclusion levels of SIRT1, which is a potential therapeutic target In conclusion, 847 mRNAs, 41 lncRNAs, and 38 miRNAs for combating insulin resistance, and thereby improv- were signifcantly diferentially expressed in peripheral ing hepatic insulin sensitivity [33]. It is well known that blood mononuclear cells of T1DM patients. A disease hyperglycaemia, acidosis, and insulin resistance play pathway network was made based on the T1DM-related signifcant roles in T1DM [34]. In a study by Nielsen ceRNA regulatory network, which consisted of four et al., 12 upregulated miRNAs, including miR-152, miR- lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1), 30a-5p, miR-181a, miR-24, miR-148a, miR-210, miR- one miRNA (hsa-miR-181a), six target genes, as well 27a, miR-29a, miR-26a, miR-27b, miR-25, and miR-200a, as three important pathways related to T1DM, includ- were identifed in T1DM patients [35]. Tus, the changes ing the phosphatidylinositol signaling system (involving in expression of hsa-miR-181a in this study were consist- PIP4K2A, INPP4A, PIP4K2C, and CALM1), the dopa- ent with the results of previous studies. minergic synapse (involving CALM1 and PPP2R5C), Shi and Yao BMC Med Genomics (2021) 14:133 Page 9 of 10

and the insulin signaling pathway (involving CBLB and https://www.ncbi.nlm.nih.gov/geo/). The raw data were collected and ana- lyzed by the Authors, and are not ready to share their data because the data CALM1). Tese results suggest that the signature RNAs have not been published. identifed above may serve as important regulators in the pathogenesis of T1DM. However, the expression changes Declarations of miRNA and mRNAs should be further detected in dif- ferent patient cohort by using laboratory experiments. Ethics approval and consent to participate Not applicable. More particularly, four lncRNAs, LINC01278, TRG-AS1, MIAT, and GAS5-AS1, may compete with hsa-miR-181 Consent for publication to regulate its target genes in T1DM. Not applicable. Competing interests The authors declare no confict of interest. Abbreviations T1DM: Type 1 diabetes mellitus; lncRNAs: Long non-coding RNAs; miRNAs: Author details MicroRNAs; CTD: Comparative Toxicogenomics Database; ceRNAs: Competing 1 Department of Clinical Laboratory, The Third Hospital of Jilin University, No. endogenous RNAs; DERs: Diferentially expressed RNAs; GO: Gene ontology; 126, Xiantai Street, Changchun 130033, Jilin, China. 2 Department of Clinical KEGG: Kyoto Encyclopedia of Genes and Genomes. Laboratory, The First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun 130021, Jilin, China. Supplementary Information Received: 30 August 2020 Accepted: 4 March 2021 The online version contains supplementary material available at https://doi. org/10.1186/s12920-021-00931-0.

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